MICE填充后GLM模型合并报错：部分正常部分异常

用MICE填充数据后，生成了「复合指标」列（基于模拟数据操作）。构建多个回归模型并合并结果时，所有模型使用相同的预测变量，但因因变量不同，部分模型可正常运行并提取汇总结果（如fit.somatic.anxiety.pooled），而fit.shaps.pooled执行pool_glm时抛出错误：Error in eval(predvars, data, env) : object 'shaps.predicted.H3Bi' not found。

已尝试的排查步骤 #

• 对比各填充数据集的因变量列，确认无缺失值，且不同填充版本的因变量相关性接近（r≈0.96）
• 检查with()命令的输出，两个模型的结果格式一致

相关代码 #

df=list2milist(imputed.withPredictions)

fit.somatic.anxiety=with(df,glm(sticsa.trait.somatic.predicted.H2B ~ group+Age+Gender+Education,family='gaussian'))
fit.somatic.anxiety.pooled <- pool_glm(fit.somatic.anxiety)
fit.somatic.anxiety.pooled$pmodel

fit.shaps = with(df,glm(shaps.predicted.H3Bi ~ group+Age+Gender+Education,family='gaussian'))
fit.shaps.pooled=pool_glm(fit.shaps)

错误回溯信息 #

Error when running pool_glm(fit.shaps):
Error in eval(predvars, data, env) : 
  object 'shaps.predicted.H3Bi' not found

traceback()
10: eval(predvars, data, env)
9: eval(predvars, data, env)
8: model.frame.default(formula = form1, data = imp.dt[[i]], drop.unused.levels = TRUE)
7: stats::model.frame(formula = form1, data = imp.dt[[i]], drop.unused.levels = TRUE)
6: eval(mf, parent.frame())
5: eval(mf, parent.frame())
4: glm(form1, data = imp.dt[[i]])
3: glm_lm_bw(data = data, nimp = nimp, impvar = impvar, Outcome = Outcome, 
       P = P, p.crit = p.crit, method = method, keep.P = keep.P)
2: glm_mi(data = imp_dat, formula = fm, p.crit = p.crit, direction = direction, 
       nimp = nimp, impvar = "imp_id", keep.predictors = keep.predictors, 
       method = method, model_type = "linear")
1: pool_glm(fit.shaps)

内容的提问来源于stack exchange，提问作者JacquieS